Encyclopedia of Database Systems

2009 Edition
| Editors: LING LIU, M. TAMER ÖZSU

Image Querying

  • Ilaria Bartolini
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-39940-9_1440

Synonyms

Definition

Image querying refers to the problem of finding objects that are relevant to a user query within image databases (Image DBs). The classical solutions to deal with such problem include the semantic-based approach, where an image is represented through metadata (e.g., keywords), and the content-based solution, commonly called content-based image retrieval (CBIR), where the image content is represented by means of low-level features (e.g., color and texture). While with the semantic-based approach the image querying problem is transformed into an information retrieval problem, for CBIR more sophisticated query evaluation techniques are required. The usual approach to deal with this is illustrated in Fig. 1: By means of a graphical user interface (GUI), the user provides a query image, by sketching it using graphical tools, by uploading an image she/he has, or by selecting an image supplied by the system. Low-level features are extracted for...
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Ardizzoni S., Bartolini I., and Patella M. Windsurf: region-based image retrieval using wavelets. In Proc. 1st Int. Workshop on Similarity Search, 1999, pp. 167–173.Google Scholar
  2. 2.
    Bartolini I., Ciaccia P., Oria V., and Özsu T. Flexible integration of multimedia sub-queries with qualitative preferences. Multimedia Tools Applicat., 33(3):275–300,June 2007.CrossRefGoogle Scholar
  3. 3.
    Bartolini I., Ciaccia P., and Patella M. A sound algorithm for region-based image retrieval using an index. In Proc. 4th Int. Workshop on Query Processing and Multimedia Issues in Distributed Systems, 2000, pp. 930–934.Google Scholar
  4. 4.
    Carson C., Thomas M., Belongie S., Hellerstein J.M., and Malik J. Blobworld: a system for region-based image indexing and retrieval. In Proc. 3rd Int Conf. on Visual Information Systems, 1999, pp. 509–516.Google Scholar
  5. 5.
    Flickner M., Sawhney H.S., Ashley J., Huang Q., Dom B., Gorkani M., Hafner J., Petkovic D., Steele D., and Yanker P. Query by image and video content: The QBIC system. IEEE Computer, 28(9):23–32,September 1995.Google Scholar
  6. 6.
    Natsev A., Rastogi R., and Shim K. WALRUS: a similarity retrieval algorithm for image databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1999, pp. 396–405.Google Scholar
  7. 7.
    Rubner Y. and Tomasi C. Perceptual Metrics for Image Database Navigation. Kluwer Academic, Boston, MA, December 2000.Google Scholar
  8. 8.
    Smeulders A.W.M., Worring M., Santini S., Gupta A., and Jain R. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Analy. Machine Intell., 22(12):1349–1380,December 2000.CrossRefGoogle Scholar
  9. 9.
    Smith J.R. and Chang S.-F. VisualSEEk: A fully automated content-based image query system. In Proc. 4th ACM Int. Conf. on Multimedia, 1996, pp. 87–98.Google Scholar
  10. 10.
    Wang J.Z., Li J., and Wiederhold G. SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Machine Intell., 23(9):947–963,September 2001.CrossRefGoogle Scholar
  11. 11.
    Weber R. and Mlivoncic M. Efficient region-based image retrieval. In Proc. Int. Conf. on Information and Knowledge Management, 2003, pp. 69–76.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Ilaria Bartolini
    • 1
  1. 1.University of BolognaBolognaItaly